Drone carrier

ABSTRACT

Embodiments of the present invention provide a method comprising receiving a task set comprising multiple tasks, receiving operational information identifying one or more operating characteristics of multiple drones, and obtaining an initial heuristic ordering of the multiple tasks based on the operational information and the climate information. Each task has a corresponding task location. The method further comprises scheduling the multiple tasks to obtain a final ordering of the multiple tasks. The final ordering represents an order in which the multiple tasks are scheduled, and the final ordering may be different from the initial heuristic ordering.

The present invention generally relates to drone management, and moreparticularly, to an apparatus for deploying and landing drones fromstationary and mobile platforms.

BACKGROUND

Current drone management systems of single or fleet of drones viaconventional radio control protocols have multiple disadvantages. Forexample, there are currently no plans for general air traffic controland no way to expand existing air traffic control systems to hightraffic volumes and potential traffic congestion required for many droneapplications such as package pick up and delivery. State-of-the-artdrones have on-board collision avoidance systems; but these systems arenot designed to function in heavily congested airspace. As anotherexample, there is currently no ubiquitous infrastructure for flight planmanagement for drones in multiple heterogeneous applications. Currentwork in this area addresses general air traffic control using humancontrollers with eventual automation, an approach that suffers from ascalability issue. Further, there are presently no widespreadinfrastructures for drone service, no services like weather forecastssuited to drone requirements, and no fail safe designs for drones toenable safe usage over populated areas.

SUMMARY

Embodiments of the present invention provide a method comprisingreceiving a task set comprising multiple tasks, receiving operationalinformation identifying one or more operating characteristics ofmultiple drones, and obtaining an initial heuristic ordering of themultiple tasks based on the operational information and the climateinformation. Each task has a corresponding task location. The methodfurther comprises scheduling the multiple tasks to obtain a finalordering of the multiple tasks. The final ordering represents an orderin which the multiple tasks are scheduled, and the final ordering may bedifferent from the initial heuristic ordering.

These and other aspects, features and advantages of the invention willbe understood with reference to the drawing figures, and detaileddescription herein, and will be realized by means of the variouselements and combinations particularly pointed out in the appendedclaims. It is to be understood that both the foregoing generaldescription and the following brief description of the drawings anddetailed description of the invention are exemplary and explanatory ofpreferred embodiments of the invention, and are not restrictive of theinvention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other objects, features, andadvantages of the invention are apparent from the following detaileddescription taken in conjunction with the accompanying drawings inwhich:

FIG. 1 illustrates an example air traffic control and flight planmanagement system 200, in accordance with an embodiment of theinvention;

FIG. 2 illustrates the air traffic control and flight plan managementsystem in detail, in accordance with an embodiment of the invention;

FIG. 3 illustrates an example representative architecture for ascalable, flexible, automated, air traffic control and flight planmanagement system for drones, in accordance with an embodiment of theinvention;

FIG. 4 illustrates a flowchart of an example process that a zonecontroller implements for locking successive four-dimensional (4D) cellsincluded in a modified flight plan for a drone, in accordance with anembodiment of the present invention;

FIG. 5 illustrates a flowchart of an example process that a zonecontroller implements for detecting change in an executable flight planfor a drone, in accordance with an embodiment of the invention;

FIG. 6 illustrates a flowchart of an example process that a zonecontroller implements for detecting failure in execution of anexecutable flight plan for a drone, in accordance with an embodiment ofthe present invention;

FIG. 7 illustrates an example framework for a drone programmingenvironment, in accordance with an embodiment of the invention;

FIG. 8 illustrates an example onboard drone adaptor on a drone, inaccordance with one embodiment of the invention;

FIG. 9 illustrates an example remote adaptor on a server of a zonecontroller, in accordance with one embodiment of the invention;

FIG. 10 illustrates an example drone management data structure system,in accordance with an embodiment of the invention;

FIG. 11 illustrates an example tree data structure, in accordance withan embodiment of the invention;

FIG. 12 illustrates an example split operation, in accordance with anembodiment of the invention;

FIG. 13 illustrates an example merge operation, in accordance with anembodiment of the invention;

FIG. 14A illustrates an example drone receiver, in accordance with anembodiment of the invention;

FIG. 14B illustrates an example top view of the drone receiver in FIG.14A, in accordance with an embodiment of the invention;

FIG. 14C illustrates an example bottom view of the drone receiver inFIG. 14A, in accordance with an embodiment of the invention;

FIG. 14D illustrates another example bottom view of the drone receiverin FIG. 14A, in accordance with an embodiment of the invention;

FIG. 14E illustrates an example drone receiver mounted onto/coupled witha non-stationary flat-bed ground or water vehicle, in accordance with anembodiment of the invention;

FIG. 15 illustrates a concept of feasibility, in accordance with anembodiment of the invention;

FIG. 16 illustrates an example application of a heuristic ordering oftasks based on a recursive polygonal spiral, in accordance with anembodiment of the invention;

FIG. 17 illustrates a flowchart of an example process that the systemimplements for managing a moving drone receiver, in accordance with anembodiment of the present invention; and

FIG. 18 is a high level block diagram showing an information processingsystem useful for implementing one embodiment of the invention.

The detailed description explains the preferred embodiments of theinvention, together with advantages and features, by way of example withreference to the drawings.

DETAILED DESCRIPTION

The present invention generally relates to drone management, and moreparticularly, to an apparatus for deploying and landing drones fromstationary and mobile platforms. Embodiments of the present inventionprovide a method comprising receiving a task set comprising multipletasks, receiving operational information identifying one or moreoperating characteristics of multiple drones, and obtaining an initialheuristic ordering of the multiple tasks based on the operationalinformation and the climate information. Each task has a correspondingtask location. The method further comprises scheduling the multipletasks to obtain a final ordering of the multiple tasks. The finalordering represents an order in which the multiple tasks are scheduled,and the final ordering may be different from the initial heuristicordering.

Embodiments of the invention may utilize an existing cellular phonenetwork as a communication medium and fundamental structure for airtraffic control zones (i.e., ground zones). The fundamental structurefor air traffic control zones is two-dimensional (2D), specified asintervals in longitude and latitude. Each drone has a correspondingunique identifier (e.g., a cell phone number) that may be used forcommunication (e.g., via text messaging). For example, each drone maycommunicate with its corresponding unique identifier in response to ahand-off from one cellular phone tower to another in the cellular phonenetwork.

Embodiments of the invention provide a scalable, flexible, automated,air traffic control and flight plan management system for drones, thesystem configured to provide a distributed service that partitions andlocks available air-space into four-dimensional (4D) cells. Each 4D cellis specified by intervals of four different dimensions. In oneembodiment, the four different dimensions comprise three spatialdimensions (e.g., longitude, latitude and elevation) and one temporaldimension (e.g., time). The 4D cells have much finer granularity thanany existing cellular phone network partition, thereby enabling airtraffic control to be maintained via fine grained management andmodification of flight plans. Air traffic control zones may shareboundary cells.

Embodiments of the invention avoid congestion by locking 4D cells in airtraffic control zones to ensure that different executable flight plansmay not share 4D cells. In one embodiment, the air traffic control andflight plan management system is configured to receive a request for anexecutable flight plan for a drone, lock 4D cells in air traffic controlzones exclusively for the flight plan, and return/provide the flightplan. The executable flight plan may be modified each time the dronemoves from one air traffic control zone into another, thereby ensuringthat the flight plan comprises best estimates for times of takeoff,landing, air traffic control zone arrival and/or air traffic controlzone departure. The air traffic control and flight plan managementsystem assumes collision avoidance is already implemented; the systemprovides sufficient congestion reduction, such that the situations inwhich avoiding collisions becomes an issue will rarely arise.

FIG. 1 illustrates an example air traffic control and flight planmanagement system 200, in accordance with an embodiment of theinvention. The system 200 comprises one or more server devices 210, andone or more storage devices 220. The storage devices 220 may maintainone or more databases 260. The system 200 may exchange data with one ormore drones 50 and/or one or more zone controllers 60 via one or morecommunication mediums (e.g., an existing cellular phone network). Asdescribed in detail later herein, one or more application units mayexecute/operate on the server devices 210 to provide a distributedservice configured to partition available air-space below apre-specified altitude and time into 4D cells, and exclusively lock oneor more of the 4D cells in one or more air traffic control zones onbehalf of a drone 50.

In one embodiment, the system 200 is configured to receive differenttypes of input and provide different types of responses. For example, inresponse to receiving as input a request for a unique identifier for adrone 50, the system 200 assigns a unique identifier to the drone 50 andresponds with the unique identifier. The unique identifier may be anytype of identifier (e.g., a cell phone number). As another example, inresponse to receiving as input a flight plan request for a drone 50, thesystem 200 responds with an executable flight plan for the drone 50. Asanother example, in response to receiving as input a detected change inan executable flight plan for a drone 50, the system 200 responds with anew and approved executable flight plan for the drone 50. As anotherexample, in response to receiving as input a detected failure of a drone50, the system 200 responds with one or more required changes to one ormore other executable flight plans for one or more other drones 50, anda notification that the drone 50 has gone missing near a last knownlocation for the drone 50.

FIG. 2 illustrates the air traffic control and flight plan managementsystem 200 in detail, in accordance with an embodiment of the invention.In one embodiment, the storage devices 220 (FIG. 1) maintain at leastone database 400 maintaining a collection of executable flight plans410, where each executable flight plan 410 corresponds to a drone 50.

The system 200 further comprises a partition module 450 configured topartition available air-space into fine grained 4D cells. In oneembodiment, each 4D cell is specified by intervals of three spatialdimensions, such as longitude, latitude and elevation, and one temporaldimension, such as time. Minimum dimensions for 4D cells must satisfythe condition that each 4D cell is large enough to allow a drone tomaintain its location within the 4D cell and to move vertically (i.e.,change its elevation) from one 4D cell to another 4D cell immediatelyabove or below the current 4D cell, without changing the horizontalintervals (e.g., latitude and longitude) defining the current 4D cell.Minimum dimensions for 4D cells may be based on one or morecharacteristics of a class of drones managed by the system 200, such asmaximum horizontal speed, maximum required tolerance for position,speed, heading, etc.

In one embodiment, the at least one database 400 maintains a collectionof data structures 420, where each data structure 420 corresponds to a4D cell and includes information relating to the 4D cell (e.g., anidentity of a drone 50 that has an exclusive lock on the 4D cell).

The system 200 further comprises a locking module 460 configured toexclusively lock 4D cells on behalf of a drone 50 (FIG. 1).Specifically, the system 200 may receive, as input, a flight planrequest for a drone 50 either from a zone controller 60 (FIG. 1) or thedrone 50. The flight plan request may include a filing of a flight planfor the drone 50 (“the initially filed flight plan”). The initiallyfiled flight plan comprises an identity of the drone 50 (e.g.,corresponding unique identifier), earliest requested flight time and/orlatest desired arrival time for the drone 50, departure location for thedrone 50, and one or more arrival locations for the drone 50. Thelocations included in the initially filed flight plan may be specifiedas three-dimensional (3D) coordinates. The locking module 460 constructsa modified flight plan for the drone 50 based on the initially filedflight plan. The modified flight plan is an approved and executableflight plan for the drone 50. The modified flight plan comprises theidentity of the drone 50 and a planned flight path for the drone 50. Theplanned flight path comprises a sequence of 4D cells, such as anapproved departure cell and a sequence of approved arrival cells. In oneexample implementation, the 4D cells represent 4D locations representedby two horizontal intervals (e.g., longitude and latitude), one verticalinterval (e.g., elevation), and one time interval. An air trafficcontrol zone or a 4D cell is on a flight path if any part of the airtraffic control zone or the 4D cell is within a pre-specified distanceof any point on the flight path.

As described in detail later herein, the locking module 460 applies analgorithm that exclusively locks each 4D cell included in the modifiedflight plan. The modified flight plan may also include additionalarrival locations. The system 200 returns/provides the modified flightplan to either the zone controller 60 or the drone 50.

The locking module 460 attempts to obtain/place an exclusive lock onbehalf of a drone 50 on each 4D cell included in a modified flight planfor the drone 50. In one example implementation, the locking module 460obtains/places an exclusive lock on a 4D cell on behalf of a drone 50 byregistering an identity of the drone 50 in the 4D cell. In oneembodiment, each 4D cell included in the modified flight plan satisfiesthe following condition: some point of the 4D cell is within apre-specified distance of a path defined by an original 2D location ofthe drone 50 and segments between 4D cells of the modified flight plan.

If the locking module 460 fails to obtain/place an exclusive lock onbehalf of a drone 50 on a 4D cell included in a modified flight plan forthe drone 50 (i.e., the 4D cell is already locked on behalf of anotherdrone 50), the locking module 460 reroutes the modified flight planaround the 4D cell to a random adjacent/neighboring 4D cell that is tothe left of, right of, above, below, or later in time than the 4D cell,from the point of view of the modified flight plan toward the 4D cell.An available set of randomly chosen adjacent/neighboring 4D cells mayinclude “later” cells with the same 3D coordinates, where each “later”cell indicates that the drone 50 is to remain in the same 3D cell (orwait before takeoff) for one time unit, the time unit being the timeduration (i.e., time interval) of the 4D cell.

In one embodiment, 4D cells at ground level are not lockable.

In one embodiment, to reroute a modified flight plan for a drone 50around a 4D cell that is already locked on behalf of another drone 50,the locking module 460 adds a new location to a planned flight pathincluded in the modified flight plan. The new location must satisfy thefollowing conditions: (1) the new location is on a shared boundarybetween a last 4D cell locked on behalf of the drone 50 and a randomlychosen adjacent/neighboring 4D cell that is not locked, and (2) an anglebetween a first segment and a second segment is not obtuse, where thefirst segment is between the last 4D cell locked on behalf of the drone50 and the randomly chosen adjacent/neighboring 4D cell, and the secondsegment is between the last 4D cell locked on behalf of the drone 50 andthe 4D cell that is already locked.

In a preferred embodiment, a planned flight path for a drone 50 is keptstraight. In another embodiment, a planned flight path for a drone 50may be adjusted to minimize the number of 4D cells locked on behalf ofthe drone 50 by making adjustments in location to keep each successive4D cell on the flight path unique.

In one embodiment, a planned flight path for a drone 50 may be adjustedto bias towards lower altitudes whenever the altitude is more than onevertical unit (i.e., the height of the relevant 4D cell) above theground, thereby protecting the drone 50 from external forces that aremagnified at higher altitudes, such as wind.

The system 200 further comprises a lock conflict module 470 configuredto maintain a pre-determined rate of lock conflict for each air trafficcontrol zone. 4D cells form a partition of air space below apre-specified altitude and time; the 4D cells are organized/grouped intorectilinear 2D zones. For example, 4D cells are organized/grouped intoair traffic control zones. To maintain a pre-determined rate of lockconflict for an air traffic control zone, the lock conflict module 470may trigger the repartitioning of the air traffic control zone into moreor fewer 4D cells, where the repartitioning is independent of other airtraffic control zones and subject to minimum dimensions for 4D cells.

In one embodiment, the system 200 maintains a quaternary tree ofhierarchical 2D zones, where the finest grained zones are of at least aminimum size required for tolerances. If a zone is a source ofsignificant contention among multiple initially filed flight plans, thezone may be refined into four zones. In another embodiment, each 4D cellof the zone may be partitioned into multiple 4D cells. In one exampleimplementation, to determine a sequence of 4D cells to include in amodified flight plan for a drone 50, a depth first search of aquaternary tree of hierarchical 2D zones is performed, following zoneson path. At leaves of the tree, 4D cells on path are determined and anidentity of the drone 50 is registered in each of the 4D cellsdetermined, thereby exclusively locking the 4D cells on behalf of thedrone 50. If an exclusive lock on a 4D cell fails to be placed/obtained,the modified flight plan is rerouted around the 4D cell and the lockconflict module 470 increments a corresponding count of lock conflictsfor the zone. If a corresponding count of lock conflicts for a zoneexceeds a pre-determined threshold, the lock conflict module 470schedules 4D cells of the zone for partition. In another exampleimplementation, optimistic concurrent breadth first search of thequaternary tree of hierarchical 2D zones is performed instead. If anexclusive lock on a 4D cell fails to be placed/obtained, reroutingaround the 4D cell includes abandoning 4D cells no longer on the path.

In this specification, let the terms “lat”, “long” and “elev” denotelatitude, longitude and elevation, respectively.

Table 1 below provides medium level detail of example pseudo-code for analgorithm that the locking module 460 applies when locking successive 4Dcells included in a modified flight plan.

TABLE 1 Input in Base State:  Current position as 4D location (time,lat, long, elev) .  Horizontal and Vertical speed of drone (hspeed,vspeed)  All required cells to this point locked  Eight surrounding 4Dlocations defining convex 4D space  At least one of eight surroundinglocations on at least one cell boundary  Target 3D position (in zone)lockNextCell:  If at target then return success  For each surroundinglocation and each new cell on the boundary with the surrounding location◯ Lock new cell ◯ If fail, then { undo previous move call reroute(cellalready locked, surrounding location, boundary) } move:  Move currentposition and rigidly move the eight surrounding locations in thedirection of the target until the base state conditions above are againsatisfied: ◯ If at same lat and long as target then heading is in elevdirection only ◯ Compute rate in each dimension (time always has rate 1)from heading and speed ◯ For each surrounding location: Compute time tonext cell boundary using rate in each dimension ◯ Find minimum time tonext cell boundary ◯ Advance current position and all surrounding pointsby minimum time Returning to base state  Iterate through steps of thealgorithm Input in Base State Input: cell X on boundary Y withsurrounding location z where locking failed reroute:  Select at randomone of the following directions: ◯ Delay (advance across time boundaryif orthogonal to Y) ◯ Up (if available and orthogonal to Y, advanceacross elevation boundary) ◯ Down (if available and orthogonal to Y,advance across elevation boundary) ◯ Lat (if available and toward targetand orthogonal to Y, advance across lat boundary) ◯ Long (if availableand toward target and orthogonal to Y, advance across long boundary) Try lockNextCell from move with trial direction replacing old heading If fail, try again without replacement until pre-specified number oftrials  If success, call lockNextCell on result state  If fail returnfail

Table 2 below provides fine level detail of example pseudo-code for analgorithm that the locking module 460 applies when locking successive 4Dcells included in a modified flight plan. For simplicity ofpresentation, it is assumed that each change of 3D location in amodified flight plan is either a vertical move only or a horizontal moveonly, not both. Further, it is generally assumed that a modified flightplan may include simultaneous horizontal moves and vertical moves withonly a slight increase in the complexity of Table 2.

TABLE 2 This level of detail assumes a binary tree for each dimensiondescribing the intervals corresponding to cells. In some embodimentshspeed and vspeed are functions of position based on estimated windvelocity. Given position (pTime, pLat, pLong, pElev), the eightsurrounding locations are: 1. (pTime+timeTh, pLat, plong, pElev) 2.(pTime−timeTh, pLat, pLong, pElev) 3. (pTime, pLat+latTh, pLong, pElev)4. (pTime, pLat−latTh, pLong, pElev) 5. (pTime, pLat, pLong+longTh,pElev) 6. (pTime, pLat, pLong−longTh, pElev) 7. (pTime, pLat, pLong,pElev+elevTh) 8. (pTime, pLat, pLong, pElev−elevTh) It is assumed thatthe thresholds timeTh, latTh, longTh, and elevTh are each smaller thanhalf the minimum 4D cell defining interval in the correspondingdimension. These thresholds allow for some error in GPS location andprediction of time required to execute a part of a flight plan. If everycell touched by any of the eight surrounding locations is locked, thenit follows from the size of the thresholds that every cell touched bythe position of the drone is locked, provided the drone's actualposition is within the thresholds of the estimated position used in theflight plan. (upV,latV,longV) partialVelocity(position3D{posLat,posLong,posElev}, target3D{tarLat, tarLong, tarElev}, hspeed,vspeed)  If (tarLat == posLat&&tarLong == posLong) then { upV =vspeed*sign(tarElev − posElev) latV = 0 longV = 0 } else If tarLat ==posLat then {upV = 0, latV = 0, longV = hspeed*sign(tarLong − posLong)}else If tarLong ==posLong then {upV = 0, latV =hspeed*sign(tarLat−posLat), longV = 0} else { upV = 0 x =(tarLat−posLat) y = (tarLong− posLong) latV = hspeed*((x/sqrt(x*x +y*y))*sign(x) longV = hspeed*((y/sqrt(x*x + y*y))*sign(y) }nextCellBoundary(position4D{posTime, posLat, posLong, posElev},target3D{tarLat, tarLong, tarElev))  mint = 0  outputset = empty setof triples of form <surrounding location, dimension, value>  For eachsurrounding location and each dimension: ◯ determine partial velocity vusing partialVelocity /* partial velocity in time dimension is always1*/ ◯ search binary tree of dimension for interval containing theprojection of surrounding location on dimension ▪ a = current projectionof surrounding location on dimension ▪ (left, right) = root interval fordimension ▪ while (left, right) has active descendants if a <=(left+right)/2 then right = (left+right)/2 else left = (left+right)/2 ▪interval = (left, right] ◯ synchLock(interval) /* for multithreading */◯ t = current projection of surrounding location on time ◯ if sign(v) >0 then b = right else b = left ◯ nt = time to next boundary = t+ (b−a)/v◯ if (nt< mint or mint == 0) synchUnlock intervals corresponding tooutputset empty outputset ◯ if (nt<= mint or mint == 0) {mint = nt, add<surrounding location, dimension, b> to outputset  output mint,outputset

Table 3 below provides an example application of the algorithm in Table2.

TABLE 3 Given:  thresholds: time(1 minute), lat(0.1 mile), long(0.1mile), elev(50 feet)  2 minute time intervals,  1 mile lat and longintervals  100 feet elevation intervals  10 mile by 10 mile by 400feet zone  position given in (minutes, miles, miles, feet) as (1, 4, 6,150)  target given in (miles, miles, feet) as (7, 10, 150)  hspeed = 2miles/minute  cells locked for drone1 ◯ <(0,2],(3,4],(5,6],(100,200]> ◯<(0,2], (4,5], (5,6], (100, 200]> ◯ <(0,2],(3,4], (6,7], (100,200]> ◯<(2,4], (3,4],(5,6],(100,200]> The following steps are taken to producethe flight plan from this base state: 1. the eight initial surroundingpositions are a. <2,4,6,150> b. <0,4,6,150> c. <1,4.1,6,150> d.<1,3.9,6,150> e. <1,4,6.1,150> f. <1,4,5.9,150> g. <1,4,6,200>, h.<1,4,6,100> 2. partialVelocity a. latV = 1.2 miles/minute b. longV = 1.6miles/minute c. elevV = 0 3. outputset a. <f,long,6>nt = 0.0625 minutes4. Move all surrounding points by +0.0625minutes time a.<2,4,6,150>→<3.0625, 4.075, 6.1, 150> b. <0,4,6,150>→<0.0625, 4.075,6.1, 150> c. <1,4.1,6,150>→<1.0625, 4.175, 6.1, 150> d.<1,3.9,6,150>→<1.0625, 3.975, 6.1, 150> e. <1,4,6.1,150>→<1.0625, 4.075,6.2, 150> f. <1,4,5.9,150>→<1.0625, 4.075, 6, 150> g.<1,4,6,200>→<1.0625, 4.075, 6.1, 200> h. <1,4,6,100>→<1.0625, 4.075,6.1, 100> 5. lock new cell on boundary <(0,2],(4,5],(6,7],(100,200]> 6.outputset a. <d,lat,4>nt = 0.025/1.2 = 1/48 = 0.028333.. 7. Move allsurrounding points by +1/48 minutes time a. d →<1.090833..,4, 6.133,150> 8. new cell on boundary <(0,2],(4,5],(6,7],(100,200]> alreadylocked 9. ...

FIG. 3 illustrates an example representative architecture 600 for ascalable, flexible, automated, air traffic control and flight planmanagement system for drones, in accordance with an embodiment of theinvention. The representative architecture 600 includes a first entity610 that provides a flight plan request filing interface. The flightplan request filing interface is configured to receive, from a drone 50(FIG. 1), a flight plan request that includes a filing of a flight planfor the drone 50. The representative architecture 600 further includesone or more zone controllers 60, each zone controller 60 controlling airtraffic for a particular air traffic control zone. The first entity 610and the zone controllers 60 may exchange data via one or morecommunication mediums (e.g., a cellular phone network). The first entity610 and each zone controller 60 may implement one or more components ofthe air traffic control and flight plan management system 200.

FIG. 4 illustrates a flowchart of an example process 500 that a zonecontroller 60 implements for locking successive 4D cells included in amodified flight plan for a drone 50, in accordance with an embodiment ofthe present invention. In process block 501, receive a flight planrequest (FPR) comprising a unique identifier for the drone 50 (“DroneID”), a sequence of 4D cells representing a planned flight path for thedrone 50 (“sequence of flight segments with constraints”), and an entry4D cell (“entry cell”) in an air traffic control zone (“zone”) that thezone controller 60 controls air traffic for. In process block 502,determine whether the entry cell is already locked with the Drone ID andthe constraints are satisfiable. If the entry cell is already lockedwith the Drone ID and the constraints are satisfiable, proceed toprocess block 503. If the entry cell is not already locked with theDrone ID and/or the constraints are not satisfiable, proceed to processblock 504.

In process block 504, send a “Fail” notification/message/report to aflight plan request filing interface 610 (FIG. 3) or a prior zonecontroller 60 controlling air traffic for a prior zone.

In process block 503, determine whether there are more 4D cells on pathin the zone. If there are more 4D cells on path in the zone, proceed toprocess block 505. In process block 505, lock the next 4D cell for theFPR, and proceed to process block 507. If there are no more 4D cells onpath in the zone, proceed to process block 506.

In process block 506, send the FPR to a next zone controller 50controlling air traffic for a next zone, if any; otherwise, send a“Success” notification/message/report with the FPR to a flight planrequest filing interface 610 or a prior zone controller 60 controllingair traffic for a prior zone.

In process block 507, determine whether there is a conflict. If there isa conflict, proceed to process block 508. If there is no conflict,return to process block 503.

In process block 508, reroute the FPR from the locked entry cell or arandom neighboring 4D cell (“neighboring cell”). In process block 509,increment counters for the zone and the FPR, wherein each countermaintains a reroute count. In process block 510, determine if thereroute count for the zone exceeds a pre-determined threshold. If thereroute count for the zone exceeds a pre-determined threshold, proceedto process block 511. If the reroute count for the zone does not exceeda pre-determined threshold, proceed to process block 512.

In process block 511, schedule the zone for partition.

In process block 512, determine if the reroute count for the FPR exceedsa pre-determined threshold and fork. If the reroute count for the FPRexceeds a pre-determined threshold and fork, proceed to process block504. If the reroute count for the FPR does not exceed a pre-determinedthreshold and fork, proceed to process block 513.

In process block 513, lock random neighboring cell. In process block514, determine whether there is a conflict. If there is a conflict,proceed to process block 508. If there is no conflict, proceed toprocess block 515.

In process block 515, receive the FPR with the locked random neighboringcell as the entry cell.

FIG. 5 illustrates a flowchart of an example process 700 that a zonecontroller 60 (FIG. 1) implements for detecting change in an executableflight plan for a drone 50 (FIG. 1), in accordance with an embodiment ofthe present invention. In process block 701, receive a drone 4D positionreport. In process block 702, determine whether the report is consistentwith a FPR for the drone 50. If the report is consistent with a FPR forthe drone 50, proceed to process block 703 where the process 700 ends.If the report is not consistent with a FPR for the drone 50, proceed toprocess block 704 where the FPR is rerouted from a start (i.e., current)4D cell of the drone 50.

FIG. 6 illustrates a flowchart of an example process 800 that a zonecontroller 60 (FIG. 1) implements for detecting failure in execution ofan executable flight plan for a drone 50 (FIG. 1), in accordance with anembodiment of the present invention. In process block 801, receive adrone out of control report. In process block 802, override locks inaffected area within air traffic control zone that the zone controller60 controls air traffic for. In process block 803, for each flight planaffected fork, emergency reroute the FPR from a start (i.e., current) 4Dcell of the drone 50.

Embodiments of the invention provide a high level programming languagefor execution by a drone, the high level programming language compatiblewith a scalable, flexible, automated, air traffic control and flightplan management system for drones. One embodiment is configured toconvert a program in the high level programming language and comprisinga set of specifications for a drone to either an executable flight planor an explanation of infeasibility (e.g., a report or notificationexplaining why an executable flight plan for the drone is not possible).

Embodiments of the invention provide a high level programming languagefor designing a flight plan for a drone 50, and generating a flight planrequest for the drone 50 that includes the flight plan. The high levelprogramming language is compatible with the air traffic control andflight plan management system 200, such that the system 200 isconfigured to receive, as input, flight plan requests including flightplans designed using the high level programming language.

FIG. 7 illustrates an example framework 550 for a drone programmingenvironment, in accordance with an embodiment of the invention. Theframework 550 may be incorporated into the air traffic control andflight plan management system 200, or may stand alone and operate inconjunction with the system 200.

The framework 550 comprises a design unit 590 configured to design aflight plan for a drone 50 (FIG. 1) using the high level programminglanguage, and generate a flight plan request for the drone 50 thatincludes the flight plan.

In one embodiment, the high level programming language comprisesdifferent high level control instructions representing actions that areunderstandable and describable by humans.

Table 4 below provides a listing of some example primitives and controlinstructions of the high level programming language.

TABLE 4 Primitive/Control Instruction Definition Takeoff (x) x denotes aspecified elevation above ground level. Elevation x. may have multipleoptional syntaxes. Takeoff (x) represents an action that causes a droneto takeoff and rise up to the elevation x. For simplicity andcomputational efficiency, we assume that drones either move verticallyor horizontally and do not combine the two motions. It will beunderstood that allowing full 3D motion is straightforward. From loc1takeoff and loc1 denotes a specified location. The location loc1 mayhave rise elevchange1 feet multiple optional syntaxes. The location loc1is converted to latitude and longitude. elevchange1 denotes a netelevation. Net elevation elevchange1 may have multiple optionalsyntaxes. From loc1 takeoff and rise elevchange1 feet represents anaction that causes a drone to takeoff from location loc1 and rise up bythe net elevation elevchange1 in feet. The drone will take off and movevertically (maintaining its latitude and longitude within the latitudeand longitude intervals describing its original 4D cell) until it hasreached the desired elevation above the ground; it will then execute anext command that may involve horizontal motion. Land (y) y denotes anoptional specified beacon. The beacon y may have or multiple optionalsyntaxes. Land ( ) If the beacon is stationary, y may optionally beconverted to latitude and longitude; the alternative is a specificoperation of iterative estimation of a target latitude and longitudefollowed by a change of altitude and then a change of heading. Thelatter alternative is necessary if y is mobile. A mobile landing beaconis the subject of two additional invention disclosures. For thisspecification, we can assume that the beacon is stationary. Land (y)represents an action that causes a drone to land at the beacon y. Land () represents an action that causes a drone to land at a current latitudeand longitude. From loc2 land at loc2 denotes a specified location. Thelocation loc2 may have beacon2 multiple optional syntaxes. The locationloc2 is converted to latitude and longitude. beacon2 denotes a specifiedbeacon. The beacon beacon2 may have multiple optional syntaxes. Fromloc2 land at beacon2 represents an action that causes a drone atlocation loc2 to land at the beacon beacon2. From loc1 proceed to loc2loc1 denotes a first location, and loc2 denotes a second location. Thelocations loc1 and loc2 may have multiple optional syntaxes. Eachlocation is converted to latitude and longitude. From loc1 proceed toloc2 represents an action that causes a drone to move from loc1 to loc2.Move horizontal from location1 denotes a first location, and location2denotes a location1 to location2 second location. The locationslocation1 and location2 may have multiple optional syntaxes. Eachlocation is converted to latitude and longitude. Move horizontal fromlocation1 to location2 represents an action that causes a drone to movehorizontally only from the first location location1 to the secondlocation location2 while maintaining a current elevation above groundlevel. Move vertical from elevation1 denotes a first elevation, andelevation2 denotes a elevation1 to elevation2 second elevation.Elevations elevation1 and elevation2 may have multiple optional syntaxesthat are converted to net elevation in units (e.g., feet, meters, etc.)above ground level. Move vertical from elevation1 to elevation2represents an action that causes a drone to move vertically from thefirst elevation elevation1 to the second elevation elevation2 whilemaintaining a current latitude and longitude. G G or Grasp packagerepresents an action that causes a drone to or grasp a payload orpackage (e.g., an object for delivery) Grasp package R R or Releasepackage represents an action that causes a drone to or release a payloador package (e.g., an object for delivery) Release package C (z) zdenotes a custom, user-defined action (e.g., turn on/off spray). C (z)represents an action that causes a drone to execute the custom,user-defined action z. Complete by (t) t denotes a real-time deadline bywhich a flight plan for a drone must be completed. Complete by (t) is anoptional primitive that may be used to determine timing feasibility of aflight plan for a drone.

In this specification, let the term “program” denote a flight plan for adrone 50 that is designed using the high level programming language. Aprogram comprises at least one sequence of at least one instance of atleast one primitive and/or control instruction of the high levelprogramming language. The program represents operating specificationsfor a drone 50 that may include one or more interpretations of one ormore custom, user-defined actions for the drone 50 to execute.

Table 5 below provides an example program for a drone 50.

TABLE 5 from loc1 takeoff and rise elevchange1 feet from loc1 proceed toloc2 from loc2 land at beacon2 grasp package from loc2 takeoff and riseelevchange2 feet from loc2 proceed to loc1 from loc1 land at beacon1release package

The program in Table 5 comprises a sequence of control instructionsthat, when an executable flight plan (comprising a sequence of 4D cells,see Table 6) is returned and executed, cause a drone 50 to operate asfollows: (1) takeoff from location loc1 and rise up by net elevationelevchange1 in feet, (2) move from location loc1 to location loc2, (3)from location loc2, land at beacon beacon2, (4) grasp package at beaconbeacon2, (5) takeoff from location loc2 and rise by net elevationelevchange2 in feet, (6) move from location loc2 to location loc1, (7)from location loc1, land at beacon beacon1, and (8) release the packageat beacon beacon1.

In one embodiment, a program is initially checked for consistencyagainst a state machine 593. In another embodiment, a program may omitone or more specified locations and/or elevations that are automaticallyfilled in/provided by the state machine 593 based on an initial locationand elevation.

The framework 550 further comprises a compiler 591 for compiling aprogram into a flight plan request that takes into account one or moreof the following factors: horizontal and vertical speeds, reported windspeeds, weather, temporary obstacles, etc. The flight plan request isforwarded to the system 200 to obtain an executable flight plan withexclusive locks on 4D cells in air traffic control zones touched by theflight plan.

The framework 550 maintains a collection of drone profiles 560. Eachdrone profile corresponds to a drone 50, and maintains one or more ofthe following pieces of information relating to the drone 50: usefulbattery time (the battery time may account for a user-specifiedcushion), battery life as a function of recharge time at any plannedrecharge facility, horizontal air speed (assuming no wind speed, gusts,etc.), vertical climb speed (assuming no wind speed, gusts, thermals,etc.), vertical descent speed (assuming no wind speed, gusts, thermals,etc.), number of rotors, and an Application Programming Interface (API)specific to the drone 50.

The framework 550 further maintains a collection of drone weatherprofiles 565. Each drone weather profile 565 corresponds to a drone 50,and maintains one or more of the following information relating toeffects of different weather conditions on the drone 50: estimatedeffect of horizontal wind gust on various drone speeds, estimated effectof prevailing horizontal wind on drone speeds, estimated effect of updraft on various drone speeds, estimated effect of down draft on variousdrone speeds, estimated effect of steady up wind on various dronespeeds, and estimated effect of steady down wind on various dronespeeds.

The framework 550 is configured to receive and maintain zone-wideweather forecast information 575 for an air traffic control zone. Thezone-wide weather forecast information 575 includes wind velocity andintensity (e.g., maximum amplitude and direction of gusts) for the airtraffic control zone. The framework 550 further maintains a weathermodel 580 for the air traffic control zone. The weather model 580 isbased on observations on the weather conditions of the air trafficcontrol zone (e.g., the zone-wide weather forecast information 575), andis used to predict how wind conditions may change with elevation,horizontally in each direction, and daily/seasonally with time.

The framework 550 further comprises an interpolate and extrapolate unit594 configured to interpolate in space and extrapolate in time weatherconditions at any 4D cell within an air traffic control zone that is ona flight path for a drone 50. Specifically, when a flight plan calls fora drone 50 with a corresponding drone profile 560 to use the air trafficcontrol zone, the interpolate and extrapolate unit 594 determines, basedon the drone profile 560, a sequence of time-stamped GPS coordinatesrepresenting a flight path of the drone 50 within the zone, by inferringweather conditions at each 4D cell on the flight path. For example, theinterpolate and extrapolate unit 594 is configured to extrapolate intime wind conditions at any 4D cell on the flight path based on theweather model 580. As another example, the interpolate and extrapolateunit 594 is configured to interpolate in space wind conditions at any 4Dcell on the flight path based on a latitude or longitude line throughthe 4D cell and between two nearby 4D cells within the zone based onindependent observations of wind conditions at the two/nearby 4D cells,or based on one independent observation and one prediction from theweather model 580, or based on two predictions from the weather model580. The ability to interpolate in space and extrapolate in time weatherconditions at any 4D cell on a flight path for a drone 50 removes theneed to predict weather conditions for all 4D cells within the airtraffic control zone (i.e., weather conditions for unused 4D cells thatare not on the flight path may be ignored).

The framework 550 further maintains a collection of 4D cell weatherprofiles 570. Each 4D cell weather profile 570 corresponds to a 4D cellwithin an air traffic control zone, and maintains information relatingto wind conditions at the 4D cell, such as estimated prevailing (net)wind direction and speed, and estimated gust intensity direction andfrequency. As described above, the wind conditions at a 4D cell may beinterpolated in space and extrapolated in time based on the weathermodel 580 and/or independent observations of wind conditions at nearby4D cells.

The framework 550 further comprises a processing unit 592 configured todetermine overall feasibility of a program. Determining the overallfeasibility of a program takes place within the system 200 where locksare obtained. The system 200 determines which locks to obtain, takinginto account weather conditions likely to be encountered to predictwhich 4D cells need to be locked. The method of constructing the flightplan and obtaining locks is extended to include horizontal and verticalspeeds for the drone that depend on the 4D cell in which the drone willmove. For each action (i.e., control instruction) included in theprogram, the processing unit 592 estimates a speed at which the actionwill be performed at based on horizontal and vertical speeds that dependon reported wind speeds, weather conditions, temporary obstacles, etc.The system 200 is utilized to obtain exclusive locks for 4D cells on theflight plan constructed for the drone 50, and to generate and return anexecutable flight plan including a time window for initial takeoff ofthe drone 50. Before an executable flight plan is returned, if theprocessing unit 592 detects a failure at any stage of the program, areport including an explanation of infeasibility is generated andreturned instead (i.e., each failure detected is reported in detail),and any 4D cell locked on behalf of the drone 50 is released.

The processing unit 592 is configured to detect different types offailures. For example, the processing unit 592 is configured to predict,for each maximum segment of a flight plan for a drone 50, a worst casetime by which the drone 50 must reach a 4D cell located at an end of thesegment without recharging a battery of the drone 50. If the worst casetime predicted exceeds useful battery time for the drone 50, theprocessing unit 592 flags this as a detected failure and returns areport including an explanation of infeasibility. As another example,the processing unit 592 is configured to determine whether weatherconditions exceed conditions for controlled flight or whether there is asevere weather warning for part of a flight plan for a drone 50. Ifweather conditions exceed conditions for controlled flight or there is asevere weather warning for part of a flight plan for a drone 50, theprocessing unit 592 flags this as a detected failure and returns areport including an explanation of infeasibility. If an executableflight plan is returned and there is a chance that weather conditionswill worsen, the plan may include one or more contingency landing pointswhere the drone 50 may seek shelter.

The framework 550 further comprises a heuristic probe unit 595configured to heuristically probe for observed weather conditions near anewly requested 4D cell. The framework 550 maintains a weather hashtable 585 associated with a latitude binary tree, a longitude binarytree, and an elevation binary tree. Each hash entry includes an observedweather condition and a corresponding time stamp. Each hash entry has acorresponding hash key specified in intervals of longitude, latitude andelevation. A newer observed weather condition overwrites an olderobserved weather condition. Observed weather conditions that are olderthan a specified time are deleted from the weather hash table 585.

Each leaf entry in each binary tree (i.e., latitude binary tree, alongitude binary tree, and an elevation binary tree) maintains a firstlist of hash keys for hash entries including observed weather conditions(at 3D cells) and a second list of hash keys for locks placed (on 4Dcells). When two leaf entries of a binary tree are merged, an average ofobserved weather conditions is maintained for the merged leaf entry ifboth leaf entries have observations with the same time stamp; otherwise,the more recent observed weather condition survives.

In response to a request to heuristically probe for observed weatherconditions near a newly requested 4D cell, the heuristic probe unit 595is configured to determine a latitude or longitude direction that ismost orthogonal to a direction of a requested flight plan. If a latitudedirection is most orthogonal to a direction of a requested flight plan,an entry corresponding to the newly requested 4D cell is located in thelatitude binary tree, and the closest observed weather conditions areused, if any. If a longitude direction is most orthogonal to a directionof a requested flight plan, an entry corresponding to the newlyrequested 4D cell is located in the longitude binary tree, and theclosest observed weather conditions are used, if any. If observedweather conditions on opposite sides of the newly requested 4D cell areavailable at any distance from the newly requested 4D cell weatherconditions for the newly requested 4D cell are interpolated in spaceusing the interpolate and extrapolate unit 594 based on the closestobserved weather conditions on each side. If no observed weatherconditions are found, the zone-wide weather forecast information 575 isextrapolated in time using the interpolate and extrapolate unit 594.

Embodiments of the invention provide a drone adaptor configured to adaptan executable flight plan for a drone 50 to drone specific API 650 (FIG.8) for the drone 50. In one embodiment, the drone adaptor is an onboarddrone adaptor comprising a computer on a chip on the drone 50. Inanother embodiment, the drone adaptor is a remote adaptor implementingsynchronous radio translation using a compute thread on a server 680(FIG. 9) of a zone controller 60.

FIG. 8 illustrates an example onboard drone adaptor 660 on a drone 50,in accordance with one embodiment of the invention. The onboard droneadaptor 660 is configured to convert high level time and locationsensitive commands in the high level programming language to commandsfor a drone specific API 650 that lacks time and/or locationsensitivity. The onboard drone adaptor 660 comprises a GPS unit 661 forenabling time and location sensitivity. The onboard drone adaptor 660further comprises compass or gyroscopic sensors 662 for enablingorientation sensitivity to select a specific heading (i.e., direction oftravel) on drones that have no built-in compass or other orientationdevice.

FIG. 9 illustrates an example remote adaptor 670 on a server 680 of azone controller 60, in accordance with one embodiment of the invention.The remote adaptor 670 is configured to convert high level time andlocation sensitive commands in the high programming language to commandsfor a drone specific API 650 (FIG. 8) that lacks time and/or locationsensitivity. The remote adaptor 670 comprises a triangulation unit 671configured to obtain location information for a drone 50 usingtriangulation from cell towers in or near an air traffic control zonecontrolled by the zone controller 60, thereby enabling time and locationsensitivity. The remote adaptor 670 further comprises an orientationunit 672 for inferring an orientation of the drone 50 based on observedflight of the drone 50, thereby enabling orientation sensitivity.

Both the onboard drone adaptor 660 (FIG. 8) and the remote adaptor 670are configured to provide commands to the drone specific API 650. Thecommands provided to the drone specific API 650 may include low levelcommands addressing servos at individual rotors of the drone 50, such aslow level commands that increase or decrease rotational speed, or changeorientation of the rotors, with respect to a chassis of the drone 50.Both the onboard drone adaptor 660 and the remote adaptor 670 areconfigured to convert high level commands of an executable flight planto middle level command; the middle level commands are in turn convertedto low level commands for the drone specific API 650.

Table 6 below provides some example high level commands of executablecode for the example program in Table 5.

TABLE 6 Cell(latival1, longival1, elevival1, timeival1) Cell(latival1,longival1, elevival2, timeival2) Cell(latival2, longival2, elevival2,timeival3) Cell(latival2, longival2, elevival3, timeival4) Land(beacon2)...

Table 7 below provides example middle level commands converted fromexample high level commands.

TABLE 7 Time(timeival1[1]) → Up full power Elev(elevival2[1]) → Hover;Heading head(latival2−latival1,longival2−longival1), Forward full power

Embodiments of the invention provide a data structure to support avariably partitioned multi-dimensional space, where some cells of thespace include transient data. One embodiment provides a data structurethat supports the air traffic control and flight plan management system200. The data structure allows fast access to cells in a variablypartitioned multi-dimensional space that are within proximity to a givenpath in the space. The data structure also allows local repartitioningin parts of the space that are accessed frequently. The data structuretakes advantage of the transient and sparse nature of data included inthe space.

One embodiment implements adaptive management and modification of flightplans to maintain air traffic control for an air traffic control zone bypartitioning a map of available air space within the zone into multiple4D cell structures with dynamically changing granularity. The 4D cellstructures may be subdivided (i.e., locally repartitioned) or merged(i.e., locally merged) to reduce traffic congestion while minimizingrequired compute power. Each 4D cell structure has a density that variesin each dimension (spatial and temporal) based on volume of localtraffic and frequency of conflicts within the zone. For example, inareas of high conflict, 4D cell structures within the areas may berefined via local repartitioning, one dimension at a time as needed. Asanother example, in areas where frequency of conflicts is reduced, 4Dcell structures may be locally merged, one dimension at a time asneeded. To preserve a required lack of conflicts in individual 4D cellstructures, a local merger may take place at a scheduled time after alast active time.

FIG. 10 illustrates an example drone management data structure system750, in accordance with an embodiment of the invention. The system 750may be incorporated into the air traffic control and flight planmanagement system 200, or may stand alone and operate in conjunctionwith the air traffic control and flight plan management system 200.

The system 750 comprises a construct unit 751 configured to construct atree data structure 850 (FIG. 11) for each dimension of a variablypartitioned multi-dimensional space. Each tree data structure 850comprises one or more nodes 851 (FIG. 11). A node 851 may be either aparent node 851A (FIG. 11) or a leaf node 851B (FIG. 11). As describedin detail later herein, each node 851 in a tree corresponding todimension D is defined by an interval in dimension D 852 (FIG. 11) andeach leaf node maintains a list 853 (FIG. 11) of 4D cells with transientunexpired data.

In one embodiment, the tree data structure 850 may be a binary tree. Inanother embodiment, the tree data structure 850 may be another tree datastructure type, such as a ternary tree. When not specified we assume forsimplicity that the trees are binary trees. The conversion of thedescriptions below to apply to ternary or other tree data structuretypes is straightforward.

As stated above, in one embodiment, available air-space within an airtraffic control zone is partitioned into multiple 4D cells, wherein each4D cell is specified by intervals of three spatial dimensions (i.e.,longitude, latitude and elevation) and one temporal dimension (i.e.,time). The construct unit 751 is configured to construct a first treedata structure 850 for the latitude dimension, a second tree datastructure 850 for the longitude dimension, a third tree data structure850 for the elevation dimension, and a fourth tree data structure 850for time.

Four binary trees corresponding to three spatial dimensions and onetemporal dimension are sufficient to maintain the locks on 4D cellsrequired by our system for drone air traffic control. The root node inthe latitude binary tree is a latitude interval that covers the airtraffic control zone latitudes. The root node in the longitude binarytree is a longitude interval that covers the air traffic control zonelongitudes. The root node in the elevation binary tree is an elevationinterval that covers the air traffic control zone elevations (withground level always regarded as zero elevation). The root node in thetemporal binary tree is an interval of time sufficient to cover theremaining time in all current flight plans. This interval can beexpanded and changed as needed and may exceed 24 hours. The other rootsare fixed and reflect the dimensions of the zone, which is assumed to bea rectangular solid (3D figure). For the purpose of recording locks on4D cells, a 4D hash table is constructed. When a lock is set, the hashkey is the concatenation of the set of four intervals (in a specifiedorder) that describe the 4D cell. The hash key and the identity of thelock are placed in the hash table as a key, value pair. Each binary treeleaf points to a list of hash keys for all locked 4D cells. When aninterval of time describing a 4D cell has expired, any correspondingentry in the hash table has expired. Thus all data in the 4D hash tableis transient. Garbage collection removes the hash key from any binarytree leaf list and removes the entry from the hash table.

The three spatial binary trees described above may also be used torecord transient-weather observation information, each piece ofinformation comprising a time stamp and a 3D cell. For this purpose, a3D hash table is constructed, the hash key consisting of theconcatenation of the three spatial intervals describing a 3D cell (in aspecified order). The value corresponding to the hash key comprises atime stamp and details of a weather observation in the corresponding 3Dcell. When a time stamp has aged beyond a pre-specified time, it isdeemed to have expired and the corresponding entry in the 3D hash tablemay be garbage collected, as above.

When a leaf node in a binary tree is split, the corresponding intervalis split in half, each half becoming the interval for a new leaf node.All the entries in the list of hash keys of the parent node must bechanged to two entries, one for each new leaf. Entries corresponding tothe parent must be garbage collected after their corresponding valueshave been placed in the hash table for each of the two keys.

When two leaf nodes with the same parent are to be merged, the 4D caserequires that the merger wait until the old data at the leaves hasexpired while accumulating any new data at the parent. The merger in the3D case is simpler. If there is no conflict the value is promoted to theparent. In a conflict the newer timestamp wins and ties are averaged. Inall cases keys must be modified to reflect the new binary treestructure.

In another embodiment, the 3D case and the 4D case may be kept separatewith independent sets of 3 and 4 binary trees, respectively

The system 750 further comprises an activity unit 754 configured tomaintain a measure of activity for each leaf node 851B of each tree datastructure 850 constructed.

When a measure of activity for a leaf node 851B of a tree data structure850 exceeds a pre-specified high threshold, the leaf node 851B is split(i.e., locally repartitioned) into n leaf nodes utilizing a split unit752 of the system 750, wherein n is based on the tree data structuretype of the tree data structure 850. For example, if the tree datastructure 850 is a binary tree, the leaf node 851B is split into twoleaf nodes 851B. As another example, if the tree data structure 850 is aternary tree, the leaf node 851B is split into three leaf nodes 851B.

When a measure of activity for each of n leaf nodes 851B that resultfrom a split falls below a pre-specified low threshold, the n leaf nodesare merged into one leaf node 851B utilizing a merge unit 753 of thesystem 750.

FIG. 11 illustrates an example tree data structure 850 constructed bythe drone management data structure system 750, in accordance with anembodiment of the invention. The tree data structure 850 is a binarytree comprising multiple nodes 851, such as a parent node 851A (“A”), afirst leaf node 851B (“B”) and a second leaf node 851B (“C”).

The tree data structure 850 is associated with a dimension (e.g.,latitude, longitude, elevation or time). Each node 851 corresponds to aninterval 852 of the associated dimension. Each dimension interval 852 isa pair of constraints that may be generally represented as (a, b],wherein a variable x satisfies (a, b] if and only if x>a and x<=b.

For example, the parent node 851A corresponds to a first time interval852 (“Interval 1”), the first leaf node 851B corresponds to a secondtime interval 852 (“Interval 2”), and the second leaf node 851Bcorresponds to a third time interval 852 (“Interval 3”).

Each node 851 that is either a parent of leaves or a leaf may have acorresponding time constraint that may be generally represented as (a orb]. This time constraint applies to a corresponding list of hash keys.In one embodiment, this time constraint is only used for 4D hash keys.We now assume only the 4D hash keys are being discussed. A variable xsatisfies the constraint (a if and only if x>a. A variable x satisfiesthe constraint b] if and only if x<=b. When a time constraint isrepresented as (t where t denotes a time in the past, the timeconstraint is dropped. When a time constraint is represented as t] wheret denotes a time in the past, the time constraint and the correspondinglist of hash keys are garbage collected.

Each parent of leaf nodes or leaf node 851 has a corresponding list 853of 4D cells that is governed by a corresponding time constraint. Each 4Dcell is represented as an ordered sequence of four intervals, whereineach interval represents a dimension. 4D cells included in a list 853that is governed by a time constraint represented as (t are onlyavailable for access after time t. 4D cells included in a list 853 thatis governed by a time constraint represented as t] are only availablefor access at or before time t. A 4D cell is only included in a list 853if a lock is placed on the 4D cell on behalf of a drone 50 (i.e., the 4Dcell is active).

Each node 851 further comprises a pointer 854. If the node 851 is a leafnode 851B, the pointer 854 is set to NULL. If the node 851 is a parentnode 851A, the pointer 854 references immediate descendants of theparent node 851A (i.e., each leaf node 851B that descends from theparent node 851A).

FIG. 12 illustrates an example split operation, in accordance with anembodiment of the invention. Let Interval 1 denote a first interval 852of, e.g., latitude, wherein Interval 1=(a, b]. Let Interval 2 denote asecond latitude interval 852, wherein Interval 2=(a, c], and c=(a+b)/2.Let Interval 3 denote a third latitude interval 852, wherein Interval3=(c, b].

Let List 1 denote a first list 853 of 4D cells with latitude interval(a,b], i.e., the interval for the dimension being split. Let List 2denote a second list 853 with the latitude interval (a,b] replaced by(a, c] in List 1. Let List 3 denote a third list 853 with the latitudeinterval (a,b] replaced by (c, b] in List 1. Each 4D cell included inList 1 is split into two parts, i.e., one in List 2 and one in List 3.Let List 4 denote an empty list 853 after the split operation.

The split operation only takes place when a time constraint governingList 1 is inactive (i.e., any time expressed in the time constraint isin the past). For ease of illustration, time constraints for the nodes851 involved are not depicted.

The hash table information corresponding to List 1 is duplicated in thecorresponding entries for List 2 and List 3. Then the informationcorresponding to List 1 is erased and List 1 is discarded.

FIG. 13 illustrates an example merge operation, in accordance with anembodiment of the invention. Let Interval 1 denote a first latitude (forexample) interval 852 for a parent node 851A (FIG. 11) with an inactivetime constraint and an empty list 853 of 4D cells, wherein Interval1=(a, b]. Let Interval 2 denote a second latitude interval 852 for aleaf node 851B with an inactive time constraint, wherein Interval 2=(a,c], and c=(a+b)/2. Let Interval 3 denote a third latitude interval 852for a leaf node 851B (FIG. 11) with an inactive time constraint, whereinInterval 3=(c, b].

When the nodes for Interval 2 and Interval 3 are to be merged, a timeinterval (s, t] is selected in a tree data structure 850 associated withthe time dimension tree, wherein there are no active 4D cells with timedimension >=s. Let List 6 and List 7 be lists of hash keys associatedwith the two nodes to be merged so that the latitude intervals are (a,c]and (c,b], respectively. The time constraint s] is placed on each ofList 6 and List 7 Let List 5 be the empty list governed by (s.

At any time before time s, when a new hash key is to be constructedbased on a latitude f (or a latitude interval ending in f) in (a,b] andincluding a time interval (d,e] with e<s, the new hash key is placed ineither List1 (with latitude interval (a,c]) or List 2 (with latitudeinterval (c,b]), depending on whether f<=c or fc respectively, theinformation associated with the new hashkey being placed in the hashtable.

At any time before time s when a new hash key is to be constructed basedon a latitude f (or a latitude interval ending in f) in (a,b] andincluding a time interval (d,e] where e>=s, then the new hash key isplaced in List 5 (with latitude interval (a,b]), the informationassociated with the new hashkey being placed in the hash table. Sincee>=s, (d,e] is a time interval in the time binary tree, and (s,t] is atime interval in the time binary tree, d>=s.

When List 6 or List 7 is empty (i.e., the 4D cells have only expireddata), each element of the non-empty list is converted into an elementof List 5 by changing the latitude interval to (a,b]; the informationassociated with each hash key is removed from the hash table and copiedinto an entry associated with the new hash table; the leaf nodes 851B(FIG. 11) are removed; the pointer 854 (FIG. 11) is set to NULL; and thetime constraint on List 5 is removed.

In case the dimension of the binary tree is time, there is an extraoperation that is performed: as needed, a new root interval is createdthat doubles the duration of the previous root interval increasing theend point into the future, and the future half of the time binary treeis extended to the same depth as the previous half. When the previoushalf is all in the past, it is removed and the root is removed. In thisway, the time binary tree maintains a finite partition of a finite time,always extending into the future. Intervals in this partition may besplit and merged as in the case of the other dimension binary trees.

In the case of a multidimensional structure without a time dimension(e.g. a 3D structure including time-stamped weather observations orpredictions in some cells), we assume that the transient information istime-stamped or has some other method for determining when the transientinformation has expired. In one embodiment we assume time-stampedinformation and allow immediate merger of cells by selecting theinformation with the latest time stamp or combining (for example,averaging) information from multiple cells with the same time stamp.

Table 8 below illustrates example split and merge operations involving a3D representation of weather data with time stamp and expiration of data10 units later than time stamp.

TABLE 8 Initially two weather observations  at time 1 wind was observedat 20 in cell (0,100](4,8](0,4]  at time 5 wind was observed at 10 incell (100,200](4,8](0,4]  Hash table - (0,100](4,8](0,4]→ t1,wind20 -(100,200](4,8](0,4]→ t5,wind10  Elevation binary tree nodes - (0,200]<c,none> <list,empty>  (0,100] <c,none> <list,((0,100](4,8](0,4])> (100,200) <c,none> <list,((100,200](4,8](0,4])>  Split  Elevationbinary tree node (100,200] becomes a parent to two nodes  Hash table -(0,100](4,8](0,4] → t1,wind20 - (100,150](4,8](0,4] → t5,wind10 -(150,200](4,8](0,4] → t5,wind10  Elevation binary tree nodes - (0,200]<c,none> <list,empty>  (0,100] <c,none> <list,((0,100](4,8](0,4])> (100,200) <c,none> - (100,150] <c,none> <list,((100,150](4,8](0,4])> -(150,200] <c,none> <list,((150,200](4,8](0,4])> New observation andscheduled marge  at time 10 wind was observed at 5 in cell(100,150](4,8]  elevation binary tree leaves (100,150] and (150,200]are scheduled to merge into the parent node at time 20 when the currentactive data in the corresponding cells expires  Hash table -(0,100](4,8](0,4] → t1,wind20 - (100,150](4,8](0,4] → t10,wind5 -(150,200](4,8](0,4] → t5,wind10  Elevation binary tree nodes - (0,200]<c,none> <list,empty>  (0,100] <c,none> <list,((0,100](4,8](0,4])> (100,200) <c,(10> - (100,150] <c,10]> <list,((100,150](4,8](0,4])> -(150,200] <c,10]> <list,((150,200](4,8](0,4])> Empty list triggers merge at time 11 the data in (0,100](4,8](0,4] expires  at time 16 the datain (150,200](4,8](0,4] expires, triggering the scheduled merge early Hash table - (100,200](4,8](0,4] → t10,wind5  Elevation binary treenodes - (0,200] <c,none> <list,empty>  (0,100] <c,none> <list,empty> (100,200) <c,none> <list ,((100,200](4,8](0,4])>

Commercial drone management requires a system for deploying and landingdrones from stationary and mobile platforms. Embodiments of theinvention provide an apparatus for servicing, protecting andtransporting drones (e.g., drone launch, landing, storage, and/orrecharge). The apparatus may be coupled with/mounted onto a stationeryinstallation, a moving vehicle, or a larger drone for transportingsmaller drones. The apparatus may be stationary or mobile (e.g., on theground or in the air).

In one embodiment, the apparatus comprises a standardized, modular,portable, physical device that can serve as: (1) a beacon, (2) a landingarea, (3) a sheltered storage area, (4) a takeoff area, (5) a rechargefacility, and/or (6) a hangar for multiple drones. In thisspecification, let the term “drone receiver” generally denote thestandardized, modular, portable, physical device described above.

In one embodiment, the drone receiver may be mounted onto/coupled withany of the following: (1) a stationary foundation, (2) a non-stationaryflat-bed ground or water vehicle (e.g., a truck, a railroad car, etc.),or (3) a larger drone capable of carrying/storing multiple smallerdrones. In this specification, let the term “drone carrier” represent adrone receiver mounted onto/coupled with a larger drone capable ofcarrying/storing multiple smaller drones.

One embodiment relates to managing a group of drones that are associatedwith and/or carried/stored on a drone carrier. A moving drone receiverwith several stored drones (i.e., a drone carrier) may be configured toperform pick-up and delivery services for multiple source and targetlocations, wherein the stored drones have short flight times constrainedby small light weight batteries.

To execute coordinated pick-up and delivery or similar tasks using amobile apparatus (e.g., a drone carrier or a drone receiver mounted to anon-stationary flat-bed ground or water vehicle) and a group of smallerdrones, one embodiment applies a heuristic solution to the planartraveling salesman problem to obtain heuristic ordering and assignmentof the tasks to drones.

One embodiment provides a method for landing a drone on a drone carrierwhile the drone carrier is in flight. Another embodiment provides adrone carrier configured to rescue disabled drones (e.g., a drone thathas lost power in air). The drone carrier is capable of catchingpowerless drones and powered drones using shock absorbers, magneticlevitation, and/or a net.

One embodiment adapts the air traffic control and flight plan managementsystem 200 to accommodate a moving, landing or take off area with itsown flight plan.

One embodiment provides shared locks for 4D cells for group coordinatedplans.

FIG. 14A illustrates an example drone receiver 1000, in accordance withan embodiment of the invention. FIG. 14B illustrates an example top viewof the drone receiver 1000 in FIG. 14A, in accordance with an embodimentof the invention. The drone receiver 1000 comprises a body 1010. Thebody 1010 maintains a storage and recharge facility (“storage facility”)1030 that acts as a storage body for storing payload and/or drones. Thestorage facility 1030 comprises different storage locations and one ormore mechanical movable arms for storing or retrieving payload and/ordrones in the storage locations.

In one embodiment, the storage facility 1030 maintains charge on supercapacitor for quick charge/re-charge of drone battery of the storeddrones.

The drone receiver 1000 further comprises a receiver and conveyor belt1020 supported over the storage facility 1030. The conveyor belt 1020 islarge enough to receive and maintain payload and/or drones. In oneembodiment, the conveyor belt 1020 has multiple sections, and eachsection has one or more mechanical movable arms for grasping dronesand/or payload; the arms may be controlled by a landing drone or adeparting drone. For example, in one example implementation, when adrone lands on the conveyor belt 1020, the drone may signal one or morearms of the conveyor belt 1020 to grasp the drone or a payload releasedby the drone.

In one embodiment, the conveyor belt 1020 ascends from and descends intothe storage facility 1030 via a first opening 1030A and a second opening1030B, respectively, of the body 1010. Stored payload and/or drone(s)stored in the storage facility 1030 may be retrieved and placed on abelt portion of the conveyor belt 1020 before the belt portion ascendsfrom the storage facility 1030 via the first opening 1030A to rotate thestored payload and/or drone(s) to the top/upper surface of the dronereceiver 1000. Any received payload and/or drone(s) on a belt portion ofthe conveyor belt 1020 may be stored in the storage facility 1030 afterthe belt portion descends into the storage facility 1030 via the secondopening 1030B to rotate the received payload and/or drone(s) below tothe storage facility 1030.

For example, after one or more arms of the conveyor belt 1020 grasp areceived package (i.e., a received payload and/or drone), the conveyorbelt 1020 rotates the package below to the storage facility 1030 wherethe arms then release the package to one or more arms of the storagefacility 1030 for placement in a storage location within the storagefacility 1030. To bring a stored package (i.e., a stored payload and/ordrone) to the top/upper surface of the drone receiver 1000, one or morearms of the storage facility 1030 retrieve the stored package from astorage location within the storage facility 1030, and release thestored package onto the conveyor belt 1020 where one or more arms of theconveyor belt 1020 grasp the stored package as the conveyor belt 1020rotates the stored package to the top/upper surface of the dronereceiver 1000. The process of retrieving or storing a package iscontrolled by an extension of the high level programming languagedescribed above that is compatible with the air traffic control andflight plan management system 200. A drone may send parts of itsexecutable flight plan to the drone receiver 1000 that executes theparts with multiple threads and synchronization.

In one embodiment, the drone receiver 1000 comprises an array ofelectromagnets (“electromagnet array”) and provides alternating currentto power the electromagnet array. As described in detail later herein,the electromagnet array may be used to slow and stabilize a fallingdrone that is programmed to land from above the drone receiver 1000.

In one embodiment, the drone receiver 1000 comprises mechanicalretractable devices mounted at a center of drone receiver 1000. Forexample, the drone receiver 1000 may comprise one or more retractableshock absorbers, and provides power to extend and retract the shockabsorbers. As described in detail later herein, the shock absorbers maybe used to cushion the fall of a falling drone that is programmed toland from above the drone receiver 1000.

FIG. 14C illustrates an example bottom view of the drone receiver 1000in FIG. 14A, in accordance with an embodiment of the invention. In oneembodiment, the drone receiver 1000 may further comprise a chassis 1040surrounding the body 1010.

FIG. 14D illustrates another example bottom view of the drone receiver1000 in FIG. 14A, in accordance with an embodiment of the invention. Inone embodiment, multiple rotor units 1050 are placed on the chassis1040. The rotor units 1050 are spatially distributed on the chassis 1040such that, when the rotor units 1050 are powered on, thrust power fromthe rotor units 1050 lift the drone receiver 1000 and provide stability.

In one embodiment, a drone may land on the drone receiver 1000 fromabove the drone receiver 1000. To land on the drone receiver 1000 fromabove the drone receiver 1000, the landing drone and the drone receiver1000 are programmed to execute particular actions. Specifically, in oneembodiment, the landing drone is programmed to power down when it ispositioned over the drone receiver 1000 in the air; powering down thelanding drone causes the landing drone to fall. To slow and stabilizethe falling drone, the drone receiver 1000 is programmed to execute oneof the following actions: (1) releasing blasts of air to slow andstabilize the falling drone, (2) utilizing diamagnetic partiallevitation from an array of electromagnets powered by alternatingcurrent to slow and stabilize the falling drone, or (3) deploying one ormore retractable shock absorbers to cushion the fall of the fallingdrone.

In one embodiment, a drone may land on the drone receiver 1000 frombelow the drone receiver 1000. Specifically, the drone receiver 1000comprise a drone receiver 1000 mounted to a bottom of a large drone.Different variations of an umbrella or a net may be suspended below thedrone receiver 1000. The drone receiver 1000 is programmed to use theumbrella or net to envelop, catch and retrieve a disabled drone (e.g., adrone that has lost power in air), a powered down drone (i.e., a droneprogrammed to power down when it is positioned below the drone receiver1000 in the air), and a powered drone.

FIG. 14E illustrates an example drone receiver 1000 mounted onto/coupledwith a non-stationary flat-bed ground or water vehicle 1500, inaccordance with an embodiment of the invention. The vehicle 1010 may bea truck, a railroad car, a ship, etc.

Embodiments of the invention provide for scheduling of multiple tasksbased on a concept of feasibility. The concept of feasibility is basedon a planned travel segment for a mobile vehicle on which a dronereceiver is mounted, a task location of a task, predicted weathercharacteristics in the region including the travel segment and thetarget location, and operating characteristics of one of multiple dronescarried by the drone receiver. FIG. 15 illustrates the concept offeasibility, in accordance with an embodiment of the invention. A launchpoint on the travel segment is feasible for the drone with respect tothe task if there is a retrieve point at or later on the travel segment(i.e., in the direction of the scheduled heading of the mobile vehicleon which the drone receiver is mounted), such that the drone can travelfrom the launch point (e.g., segment “a” shown in FIG. 15) to the tasklocation, perform the task, and return to the retrieve point (e.g., oversegment “b” shown in FIG. 15) with a pre-specified residue of storedenergy, while the drone receiver travels from the launch point to theretrieve point, and if necessary wait for the drone at the retrievepoint, given predicted weather conditions in the region and batterycharge for the drone.

In one embodiment, the residue of stored energy is provided by assumingthat predicted wind speed in the region is always directly oppositedirection of travel of the drone. If the drone is sufficiently fasterthan the mobile vehicle (as would likely be the case with a groundvehicle), there will be no need for the mobile vehicle to wait, and theretrieve point is plotted based on the relative speeds of the twovehicles.

In one embodiment, a travel segment is scheduled for the mobile vehicleuntil the mobile vehicle arrives within a feasible distance of a tasklocation. The launch and retrieve points for a task are identical andthe mobile vehicle is scheduled to wait for the drone at this point.When other task locations come within a feasible distance of points onthe travel segment, these points are scheduled as launch points, andsuitable retrieve points are plotted and scheduled.

Embodiments of the invention are configured to apply any heuristicsolution to the traveling salesman problem, including applying heuristicsolutions to the planar traveling salesman problem. For example, FIG. 16illustrates an example application of a heuristic ordering of tasksbased on a recursive polygonal spiral, in accordance with an embodimentof the invention. For purposes of this example, assume a drone carriermust return to its original location (“Origin”) and a group of dronescarried by the drone carrier are homogenous (i.e., have the same orsimilar operating specifications). The example shown is not limited todrone carriers; it is applicable to any type of moving drone receiver1000 (e.g., a drone receiver 1000 mounted to a non-stationary flat-bedground or water vehicle).

In one embodiment, the air traffic control and flight plan managementsystem 200 is adapted to manage the drone carrier and the group ofdrones carried by the drone carrier. Specifically, the system 200 isconfigured to receive, as input, a task set comprising different tasks,wherein each task has a corresponding task target/location (“tasklocation”) associated with the task (e.g., delivering a payload to aparticular location). For example, as shown in FIG. 16, the task set maycomprise four different tasks with four different task locations—Task Awith Task Location A, Task B with Task Location B, Task C with TaskLocation C, and Task D with Task Location D. The system 200 determines aconvex polygon that covers all the task locations by computing a centerof the task locations, passing around task locations clockwise aroundthe center, and correcting when a vertex for a task location is nolonger in a convex hull of the polygon. A polygonal spiraling inwardcourse for the drone carrier is then constructed based on the polygon(e.g., triangle ABC between Task Locations A, B and C in FIG. 16). Thedifferent tasks are ordered by first encounter within a task feasibilitydistance of the drone carrier's path. Feasibility is calculated asdescribed above. Drones are assigned in accordance with the ordering ofthe tasks. For example, as shown in FIG. 16, the four different tasklocations are initially ordered as A, B, C, D in accordance with thepolygonal spiral heuristic. The actual scheduled ordering is as follows:(1) Task Location A as the first target location, (2) Task D as thesecond target location because Task Location D lies within a feasibledistance of a segment between a retrieve point for Task A and Task B,(3) Task B as the third target location, and (4) Task C as the fourthtarget location. In one embodiment, a delay along a spiral path for adrone assigned to a task is planned/programmed to allow enough time fora next drone to become available for a next task (e.g., rechargedsufficiently for next task). Part of the feasibility calculationincludes allowing for recharging time before scheduling the launch of adrone that has already performed a task.

FIG. 17 illustrates a flowchart of an example process 1200 that the airtraffic control and flight plan management system 200 implements formanaging a moving drone receiver 1000, in accordance with an embodimentof the present invention. In process block 1201, obtain an initial orderof task locations to be visited by applying a heuristic solution to theplanar traveling salesman problem. In process block 1202, determinewhether there is a next task location not yet scheduled for a visit. Ifthere is no next task location not yet scheduled for a visit, proceed toprocess block 1203. In process block 1203, schedule return to theorigin; the process ends after scheduling the return to the origin. Ifthere is a next task location not yet scheduled for a visit, proceed toprocess block 1204.

In process block 1204, set the next carrier travel segment from thecurrent location to the next task location (i.e., the location of thenext task). In process block 1205, schedule the launch of a drone whenwithin feasible distance of any task location in the order; also,retrieve points for the drone, schedule travel to at least one retrievepoint as necessary, and schedule wait for retrieval as necessary. Inprocess block 1206, determine whether there are any remaining taskswithin feasible distance of the current segment. If there is at leastone remaining task within feasible distance of the current segment,return to process block 1205.

If there are no remaining tasks within feasible distance of the currentsegment, return to process block 1202.

Embodiments of the invention allow 4D cells to be locked with a grouplock. In one embodiment, each drone member of a group of drones isregistered with its own name, a group name for the group, and itsoperating characteristics. Each drone receiver 1000 to be utilized withthe group is also registered with its own name, the group name, and itsoperating characteristics (including whether the drone receiver 1000 ismobile or stationary). In one embodiment, each mobile drone receiver1000 is configured to create an action plan and a group flight plan

The system 200 receives a request for an action plan submitted on behalfof the group. The request comprises a task set comprising differenttasks, wherein each task has one or more corresponding task locationsand one or more task actions. Some of the task locations may bespecified as a location of a drone receiver 1000 instead of specificlocation coordinates. Each task is intended to be performed by one dronemember from the group.

The system 200 attempts to satisfy the request tentatively using aheuristic solver. If a feasible solution is found, requests for flightplans are forwarded to each zone controller 60 (FIG. 1) in which a dronemember is initially located; otherwise, if not feasible solution isfound, the system 200 returns the request with a notification indicatingthat the request is infeasible.

In one embodiment, tasks are clustered by minimum distance from dronereceivers 1000.

In one embodiment, if all drone receivers 1000 utilized are stationary,each task is treated as a flight plan request, but 4D cells are stilllocked with a group lock. Each stationary drone receiver 1000 isconfigured to create a group flight plan for each of its tasks beginningwith the first task, and utilizes the least powerful drone member thatcan be expected to perform the task (i.e., a pre-specified percentage ofthe time), while allowing for re-charge before reuse, and allowing aminimum requested time between launch and landing events.

In one embodiment, if there is only one mobile drone receiver 1000,tasks are ordered by maximum distance between a task location and anorigin/initial location of the drone receiver 1000, and then clockwisespiraling in. For simplicity, assume all drone members are initiallylocated at or near the drone receiver 1000. The drone receiver 1000starts on a shortest path towards a first task location that is furthestaway from the origin. The drone receiver 1000 launches a drone memberwhen there is a feasible round trip between it and the other tasklocations. The drone receiver 1000 continues toward the retrieval pointuntil it is feasible to take a course toward a second task location thatis second furthest away (and still within distance to receive the firstdrone member launched). The drone receiver 1000 continues iteratively inthis manner until all tasks are completed.

In one embodiment, if there is more than one mobile drone receiver 1000in the group, the tasks are divided among the drone receivers 1000proportional to the number and power of drone members that are initiallylocated at or near each drone receiver 1000.

In some embodiments of the invention, the travel segments for the dronecarrier (mobile vehicle carrying the drone receiver) are perturbed fromthe method described in FIGS. 16 and 17. Instead of setting the headingtoward the first task target not yet scheduled for a visit, the headingis set toward a point that is on the direct path between the first andsecond ordered tasks (replacing the second ordered task by the originwhen no second task remains without a previously scheduled visit. Thispoint must be a feasible launch and retrieval point for the first task.In some embodiments it is always set closer to the first task than thesecond. If there is are earlier feasible launch and retrieval points forthe first task on the segment then the earliest such are scheduled. Ifnecessary the carrier waits at the retrieval point. The process is thenrepeated with a heading set toward a point on a path between the nexttask not yet scheduled for a visit and the next task after that. Whenall tasks have been scheduled, the heading is set for the origin and thecarrier is scheduled to return to the origin.

FIG. 18 is a high level block diagram showing an information processingsystem 300 useful for implementing one embodiment of the invention. Thecomputer system includes one or more processors, such as processor 302.The processor 302 is connected to a communication infrastructure 304(e.g., a communications bus, cross-over bar, or network).

The computer system can include a display interface 306 that forwardsgraphics, text, and other data from the communication infrastructure 304(or from a frame buffer not shown) for display on a display unit 308.The computer system also includes a main memory 310, preferably randomaccess memory (RAM), and may also include a secondary memory 312. Thesecondary memory 312 may include, for example, a hard disk drive 314and/or a removable storage drive 316, representing, for example, afloppy disk drive, a magnetic tape drive, or an optical disk drive. Theremovable storage drive 316 reads from and/or writes to a removablestorage unit 318 in a manner well known to those having ordinary skillin the art. Removable storage unit 318 represents, for example, a floppydisk, a compact disc, a magnetic tape, or an optical disk, etc. which isread by and written to by removable storage drive 316. As will beappreciated, the removable storage unit 318 includes a computer readablemedium having stored therein computer software and/or data.

In alternative embodiments, the secondary memory 312 may include othersimilar means for allowing computer programs or other instructions to beloaded into the computer system. Such means may include, for example, aremovable storage unit 320 and an interface 322. Examples of such meansmay include a program package and package interface (such as that foundin video game devices), a removable memory chip (such as an EPROM, orPROM) and associated socket, and other removable storage units 320 andinterfaces 322, which allows software and data to be transferred fromthe removable storage unit 320 to the computer system.

The computer system may also include a communication interface 324.Communication interface 324 allows software and data to be transferredbetween the computer system and external devices. Examples ofcommunication interface 324 may include a modem, a network interface(such as an Ethernet card), a communication port, or a PCMCIA slot andcard, etc. Software and data transferred via communication interface 324are in the form of signals which may be, for example, electronic,electromagnetic, optical, or other signals capable of being received bycommunication interface 324. These signals are provided to communicationinterface 324 via a communication path (i.e., channel) 326. Thiscommunication path 326 carries signals and may be implemented using wireor cable, fiber optics, a phone line, a cellular phone link, an RF link,and/or other communication channels.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention. The computer readable storage medium can be atangible device that can retain and store instructions for use by aninstruction execution device. The computer readable storage medium maybe, for example, but is not limited to, an electronic storage device, amagnetic storage device, an optical storage device, an electromagneticstorage device, a semiconductor storage device, or any suitablecombination of the foregoing. A non-exhaustive list of more specificexamples of the computer readable storage medium includes the following:a portable computer diskette, a hard disk, a random access memory (RAM),a read-only memory (ROM), an erasable programmable read-only memory(EPROM or Flash memory), a static random access memory (SRAM), aportable compact disc read-only memory (CD-ROM), a digital versatiledisk (DVD), a memory stick, a floppy disk, a mechanically encoded devicesuch as punch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

From the above description, it can be seen that the present inventionprovides a system, computer program product, and method for implementingthe embodiments of the invention. The present invention further providesa non-transitory computer-useable storage medium for implementing theembodiments of the invention. The non-transitory computer-useablestorage medium has a computer-readable program, wherein the program uponbeing processed on a computer causes the computer to implement the stepsof the present invention according to the embodiments described herein.References in the claims to an element in the singular is not intendedto mean “one and only” unless explicitly so stated, but rather “one ormore.” All structural and functional equivalents to the elements of theabove-described exemplary embodiment that are currently known or latercome to be known to those of ordinary skill in the art are intended tobe encompassed by the present claims. No claim element herein is to beconstrued under the provisions of 35 U.S.C. section 112, sixthparagraph, unless the element is expressly recited using the phrase“means for” or “step for.”

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method, comprising: receiving a task setcomprising multiple tasks, wherein each task has a corresponding tasklocation; receiving operational information identifying one or moreoperating characteristics of multiple drones; obtaining an initialheuristic ordering of the multiple tasks based on the operationalinformation and the climate information; and scheduling the multipletasks to obtain a final ordering of the multiple tasks, wherein thefinal ordering represents an order in which the multiple tasks arescheduled, and the final ordering may be different from the initialheuristic ordering.
 2. The method of claim 1, further comprising: foreach task, receiving climate information identifying estimated windconditions within proximity of a corresponding task location for thetask.
 3. The method of claim 2, wherein: the initial heuristic orderingis based on a recursive polygonal spiral course for a drone receivercarrying the multiple drones; and for each task, the drone receiver isscheduled to approach a corresponding task location for the task towithin a distance, the distance being computed based on one or moreoperating characteristics of a drone of the multiple drones that will bescheduled to perform the task and estimated wind conditions withinproximity of the task.
 4. The method of claim 3, wherein the distance isalso computed based on a limiting unpowered falling velocity of thedrone that will perform the task, such that the drone receiver iscapable of catching the drone if the drone encounters a power failureduring a horizontal move, wherein the horizontal move is performed at apre-specified elevation above an elevation of the drone receiver.
 5. Themethod of claim 4, wherein the drone receiver catches a drone thatencounters a power failure.
 6. The method of claim 5, wherein the dronereceiver comprises one or more mechanical shock absorbers to facilitatecatching a drone that encounters a power failure.
 7. The method of claim1, wherein: the initial heuristic ordering is computed by applying aheuristic, approximate solution to the planar traveling salesmanproblem; and the final ordering is a modification of the initialheuristic ordering based on scheduling the multiple tasks when theybecome feasible, even if out of order.
 8. The method of claim 1, furthercomprising: setting a heading toward a task location corresponding to afirst task of the initial heuristic ordering.
 9. The method of claim 1,further comprising: scheduling the launch of a drone of the multipledrones to perform a task when the drone is within a feasible distance ofthe task location independent of any initial heuristic ordering.
 10. Themethod of claim 9, further comprising: scheduling, when the carrier iswithin the feasible distance of a task location corresponding to thefirst task of the initial heuristic ordering, the launch of a drone ofthe multiple drones to perform the first task, scheduling continuing onthe same heading, and, if necessary, scheduling waiting for the returnof the drone, and scheduling subsequent re-setting of the headingtowards another task location corresponding to another task of theinitial heuristic ordering that has not already been scheduled forvisit.
 11. The method of claim 1, further comprising: managing a groupflight plan request for a group of drones utilizing shared locks for 4Dcells for the group, and computing feasibility of the request utilizinga heuristic solution that is based on maximum distance to a locationspecified in a task.
 12. A system comprising a computer processor, acomputer-readable hardware storage medium, and program code embodiedwith the computer-readable hardware storage medium for execution by thecomputer processor to implement a method comprising: receiving a taskset comprising multiple tasks, wherein each task has a correspondingtask location; receiving operational information identifying one or moreoperating characteristics of multiple drones; obtaining an initialheuristic ordering of the multiple tasks based on the operationalinformation and the climate information; and scheduling the multipletasks to obtain a final ordering of the multiple tasks, wherein thefinal ordering represents an order in which the multiple tasks arescheduled, and the final ordering may be different from the initialheuristic ordering.
 13. The system of claim 12, the method furthercomprising: for each task, receiving climate information identifyingestimated wind conditions within proximity of a corresponding tasklocation for the task.
 14. The system of claim 13, wherein: the initialheuristic ordering is based on a recursive polygonal spiral course for adrone receiver carrying the multiple drones; and for each task, thedrone receiver is scheduled to approach a corresponding task locationfor the task to within a distance, the distance being computed based onone or more operating characteristics of a drone of the multiple dronesthat will be scheduled to perform the task and estimated wind conditionswithin proximity of the task.
 15. The system of claim 12, wherein thedistance is also computed based on a limiting unpowered falling velocityof the drone that will perform the task, such that the drone receiver iscapable of catching the drone if the drone encounters a power failureduring a horizontal move, wherein the horizontal move is performed at apre-specified elevation above an elevation of the drone receiver. 16.The system of claim 15, wherein the drone receiver catches a drone thatencounters a power failure.
 17. The system of claim 12, wherein thedrone receiver comprises one or more mechanical shock absorbers tofacilitate catching a drone that encounters a power failure.
 18. Thesystem of claim 12, wherein: the initial heuristic ordering is computedby applying a heuristic, approximate solution to the planar travelingsalesman problem; and the final ordering is a modification of theinitial heuristic ordering based on scheduling the multiple tasks whenthey become feasible, even if out of order.
 19. The system of claim 12,the method further comprising: setting a heading toward a task locationcorresponding to a first task of the initial heuristic ordering.
 20. Acomputer program product comprising a computer-readable hardware storagedevice having program code embodied therewith, the program code beingexecutable by a computer to implement a method comprising: receiving atask set comprising multiple tasks, wherein each task has acorresponding task location; receiving operational informationidentifying one or more operating characteristics of multiple drones;obtaining an initial heuristic ordering of the multiple tasks based onthe operational information and the climate information; and schedulingthe multiple tasks to obtain a final ordering of the multiple tasks,wherein the final ordering represents an order in which the multipletasks are scheduled, and the final ordering may be different from theinitial heuristic ordering.